Using data science to measure the impact of opioid agonist therapy in patients admitted with Staphylococcus aureus bloodstream infections

使用数据科学测量阿片类激动剂治疗对金黄色葡萄球菌血流感染患者的影响

• 批准号: 10618404
• 负责人: David Goodman
• 金额: $ 20.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10618404
• 关键词: Acute suppurative arthritis due to bacteria; Address; Admission activity; Algorithms; Area; Award; Bacteremia; Bacteria; Bacterial Infections; Behavior; Blood; Brain; Buprenorphine; Cessation of life; Classification; Clinical Research; Coagulation Process; Communicable Diseases; Data; Data Science; Development; Diagnosis; Disease Outcome; Distant; Drug usage; Economic Burden; Electronic Health Record; Embolism; Endocarditis; Epidemic; Equipment; Equipment Contamination; Evidence based practice; Future; Geography; Goals; HIV risk; Health; Health Expenditures; Heart; Hepatitis C; Hepatitis C Transmission; Hospitalization; Hospitals; Immunosuppression; Incidence; Individual; Infection; Injecting drug user; Inpatients; Institution; International Classification of Disease Codes; Investigation; Joints; K-Series Research Career Programs; Kidney; Life; Location; Lung; Machine Learning; Measures; Medical; Medicine; Mentors; Methadone; Methods; Natural Language Processing; Opioid; Outcome; Outpatients; Overdose; Patient Admission; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Practice Guidelines; Predisposition; Productivity; Recording of previous events; Records; Research; Research Personnel; Science; Scientist; Sepsis; Services; Staphylococcus aureus; Statistical Methods; Stream; Testing; Thrombus; Time; Training; Training Programs; Travel; United States; United States National Institutes of Health; Veterans Health Administration; Water; Work; addiction; biomedical data science; blood treatment; career; classification algorithm; cohort; data repository; efficacy testing; electronic data; experience; heroin use; hospital readmission; improved; injection drug use; innovation; machine learning framework; mortality; multidisciplinary; novel; opioid agonist therapy; opioid epidemic; opioid injection; opioid misuse; opioid use; opioid use disorder; opioid user; pathogenic bacteria; pathogenic virus; prescription opioid; responsible research conduct; screening; skin microbiota; substance use treatment; time interval; treatment services; trend; viral transmission

PROJECT SUMMARY/ABSTRACT This career development award will provide early career support for investigation of the management of infec- tious diseases in the setting of addiction in hospitals. The award will provide support for the candidate to develop expertise in the following areas: 1) addiction science research; 2) natural language processing; 3) machine learn- ing; 4) professional development; and 5) responsible conduct of research. For this, Dr. Goodman-Meza will be mentored by a multidisciplinary, cross-institutional team with expertise in addiction, infectious diseases, and data science. His primary mentor, Dr. Steve Shoptaw, has an extensive track record in addiction-related research and training of future independent investigators. His co-mentors include Dr. Alex Bui and Dr. Matthew B. Goetz. Dr. Bui is an expert in biomedical data science and heads NIH training programs in this field. Dr. Goetz has broad experience of productive infectious diseases clinical research within the Veterans Health Administration (VHA). The current opioid epidemic in the United States has been associated with an increase in infections, in particular hepatitis C and bacterial infections. Bacterial infections are the leading infectious diagnosis leading to hospitali- zation in individuals with an opioid use disorder (OUD), and incur significant healthcare expenditures. Despite the availability of opioid agonist therapy (OAT) in the form of methadone or buprenorphine, less than 20% of people with OUD actually receive OAT. Hospitalization for a bacterial infection may be an ideal time to initiate OAT, but the benefits of this practice are unknown. In this proposal, the candidate will assess the impact of initiating OAT in people who inject opioids admitted to the VHA due to a Staphylococcus aureus blood stream infection (bacteremia) – the most common bacterial pathogen among people who inject opioids. Using data already collected for 36,868 cases of S. aureus bacteremia (SAB) from the VHA electronic data repository, the candidate will address three research questions: 1) is a natural language processing algorithm (NLP) more ac- curate than a standard International Classification of Diseases (ICD) code-based approach at screening records to correctly identify individuals who inject opioids in a cohort of patients admitted with SAB; 2) what are the temporal and geographic trends of SAB in people who inject opioids and those who receive OAT at the facility- level; and 3) using a machine learning framework, what are the estimated impacts of OAT on patient centered outcomes – death, readmissions, leaving against medical advice, and subsequent outpatient engagement in OAT. These formative data will help the candidate to establish a productive early career as a physician-scientist and advise development of an OAT-delivery strategy to mitigate infectious complications of injection opioid use. Through this award, Dr. Goodman-Meza will establish himself as an expert physician-scientist at the intersection of infectious disease and addiction, poised to make significant contributions to this important area of medicine.

项目总结/文摘